Evaluating the performance of combining neural networks and genetic algorithms to forecast construction demand: the case of the Singapore residential sector

In recent years, forecasting demand for residential construction in Singapore has become more vital, since it is widely perceived that the next trough of the real estate cycle is approaching. This paper evaluates the use of a combination of neural networks (NNs) and genetic algorithms (GAs) to forecast residential construction demand in Singapore. Successful applications of NNs, especially in solving complex non-linear problems, have since stimulated interest in exploring the capabilities of other biological-based methods such as GAs, and in exploiting the synergy of these two techniques to create more problem-solving power. In the study, a basic NN model is used as a benchmark to gauge the performance of the combined NN-GA model. A relative measure of forecasting accuracy, known as the mean absolute percentage error (MAPE), is used for the comparison. The models are checked also for internal validity by allowing each to be trained twice and having a set of forecasts generated after each training. Both models are found to produce accurate forecasts, because their MAPE values consistently fall within the acceptable limit of 10%. However, the combined model out-performs the basis model remarkably by reducing the average MAPE from about 6% to a mere 1%. For each model, the marginal difference in the MAPE values (i.e., 0.5% for the NN model and 0.06% for the NN-GA model) of its two forecasts indicates consistency in performance, hence establishing internal validity as well. The findings reinforce the reliability of using NNs to model construction demand and reveal the benefit of combining NNs and GAs to produce more accurate models.     

Forecasting construction industry demand,price and productivity in Singapore: the BoxJenkins approach

In academic research, the traditional Box-Jenkins approach is widely acknowledged as a benchmark technique for univariate methods because of its structured modelling basis and acceptable forecasting performance. This study examines the versatility of this approach by applying it to analyse and forecast three distinct variables of the construction industry, namely, tender price, construction demand and productivity, based on case studies of Singapore. In order to assess the adequacy of the Box-Jenkins approach to construction industry forecasting, the models derived are evaluated on their predictive accuracy based on out-of-sample forecasts. Two measures of accuracy are adopted, the root mean-square-error (RMSE) and the mean absolute percentage error (MAPE). The conclusive findings of the study include: (1) the prediction RMSE of all three models is consistently smaller than the model's standard error, implying the models' good predictive performance; (2) the prediction MAPE of all three models consistently falls within the general acceptable limit of 10%; and (3) among the three models, the most accurate is the demand model which has the lowest MAPE, followed by the price model and the productivity model.     

A modified storey enclosure model

James' Storey Enclosure Method (JSEM), developed in 1954, is considered by many to be the most sophisticated single‐rate method ever devised for early‐design‐stage tender price forecasts. However, the method is seldom used in practice partly because it has been superseded by multi‐rate methods (such as the elemental method) and partly due to the arbitrary nature of the weightings prescribed for its use. The approach has been further developed and empirical values of the weightings are derived by multivariate regression analysis. A set of 50 completed Hong Kong private housing projects is used to demonstrate the use of the technique. This involves, firstly, the modification of the variables used in the original JSEM to incorporate the special characteristics of Hong Kong multi‐storey residential buildings. This results in what is termed here as a Modified James' Storey Enclosure Model (MJSEM). Next, the optimal number of variables for inclusion in the model is identified by means of a dual stepwise cross validation regression procedure – resulting in a Regressed Modified Model for James' Storey Enclosure Method (RMJSEM). In addition, using an amended version of MJSEM, the dual stepwise cross validation regression is used to produce a Regressed Modified Model for Amended Storey Enclosure Method (RMASEM). The forecasting accuracy of RMJSEM and RMASEM is then compared with that of MJSEM together with the floor area and cube method to provide an indication of the improvement achieved. It is shown that the RMASEM provides significantly more consistent forecasts than the MJSEM and floor area models, leading to the conclusion that RMASEM may be the best model.     

A methodology for eco-efficiency evaluation of residential development at city level

Residential development provides product/service value like floor space, while at the same time it induces severe environmental impacts. This paper introduces a methodology for eco-efficiency (EE) evaluation of residential development at city level, which links product/service value and environmental impacts together. Different from previous researches on environmental impacts related to the construction process of residential buildings, the proposed methodology selects the ecological footprint (EF) as an aggregate environmental indicator to represent all resources consumed and all wastes produced by residential development, while the traditional EF model of a region is improved in view of characters of residential development. Since the final and main objective of the residential development is to provide floor space, which is chosen as the indicator of product/service value herein. The proposed methodology is applied and exemplified in the eco-efficiency evaluation of residential development in three Chinese cities, namely, Beijing, Shanghai, and Nanjing. Results derived from the proposed methodology can help policy-makers and participants in the industry to assess residential development quantitatively and roundly. They can also provide implications for the environmental management of residential development at city level.     

The first Chicago area transportation study projections and plans for metropolitan Chicago in retrospect

This paper is an evaluation of the first Chicago Area Transportation Study (CATS) projections and plans for metropolitan Chicago. The CATS work was completed during 1956 to 1962, and the projection year was 1980. The CATS forecasts of population and employment were much too high, but it turned out that the travel demand forecasts were reasonably accurate because offsetting prediction errors were made. Partly because vehicle ownership was underpredicted, CATS did not fully anticipate the increase in per capita travel demand. The CATS transportation plan derived from the predictions included an elaborate highway plan, but no part of this plan has been built as of 1987. A more modest, but still rather extensive, mass transit plan was proposed. This plan was essentially implemented. Construction of the final part of the (revised) mass transit plan is now underway. The mass transit plan had the support of the City of Chicago municipal government and funding from the US federal government. The highway plan had neither.     

The development of a combined water demandprediction system

This paper introduces a system designed to improve the accuracy of short term water demand forecasts by combining a proven mathematical prediction model with a knowledge base of information relating to non-cyclic abnormal demand occurrences. In part one of the paper, a prototype system is described that comprises a mathematical prediction module and a simplified knowledge base of FORTRAN rules. The results derived from the testing of the prototype show that the methodology is capable of providing significant improvements in prediction accuracy when the normal cyclic demand pattern is disrupted. Part two of the paper describes the steps taken towards implementing the elements that make up a full combined forecasting system in the light of the knowledge gained from building and testing the prototype.     

Dynamic demand risk assessment for toll road projects

To bridge the gap between supply of and increasing demand for roads, public–private partnership (PPP) concession contracts in which the investment cost is recovered via payments from the end users have been established. Although this mechanism has been seen as an efficient way for road projects to be completed on time and within budget, the demand risk faced during the operation stage has considerably limited this efficiency. Demand depends on a range of interrelated and dynamic factors such as the demographic and economic conditions. In addition, uncertainty is an inherent aspect of most demand-underlying factors which always make demand estimation inaccurate. However, this uncertainty is largely ignored by modellers where a single demand estimate is often used when evaluating the facility. The aim is to develop a system dynamics model to assess demand risk in road projects. The model captures the factors affecting demand and their relationships and simulates their change over time. By employing Monte Carlo simulation, the model assesses the likelihood and potential effect of an event on the outcome and provides a full picture of the various effects of potential risk. The model can help public, private, and financial stakeholders of PPP facilities make more informed decisions.     

Forecasting industrial employment figures in Southern California: A Bayesian vector autoregressive model

In this paper, we construct a Bayesian vector autoregressive model to forecast the industrial employment figures of the Southern California economy. The model includes both national and state variables. The root mean squared error (RMSE) and the Theil's U statistics are used in selecting the Bayesian prior. The out-of-sample forecasts derived from each model and prediction of the turning points show that the Bayesian VAR model outperforms the ARIMA and the unrestricted VAR models. At longer horizons the BVAR model appears to do relatively better than alternative models. A prior that becomes increasingly looser produces more accurate forecasts than a tighter prior in the BVAR estimations. Received: March 1999/Accepted: November 1999     

A user-centric space heating energy management framework for multi-family residential facilities based on occupant pattern prediction modeling

Space heating is the highest energy consumer in the operation of residential facilities in cold regions. Energy saving measures for efficient space heating operation are thus of paramount importance in efforts to reduce energy consumption in buildings. For effective functioning of space heating systems, efficient facility management coupled with relevant occupant behaviour information is necessary. However, current practice in space heating control is event-driven rather than user-centric, and in most cases relevant occupant information is not incorporated into space heating energy management strategies. This causes system inefficiency during the occupancy phase. For multi-family residential facilities, integrating occupant information within space heating energy management strategies poses several challenges; unlike with commercial facilities, in multi-family facilities occupant behavior does not follow any fixed activity-schedule pattern. In this study, a framework is developed for extracting relevant information about the uncertainties pertaining to occupant patterns (i.e., demand load) in multi-family residential facilities by identifying the factors affecting space heating energy consumption. This is achieved using sensor-based data monitoring during the occupancy phase. Based on the analysis of the monitoring data, a structure is defined for developing an occupant pattern prediction model that can be integrated with energy management strategies to reduce energy usage in multi-family residential facilities. To demonstrate the developed framework, a multi-family residential building in Fort McMurray, Canada, is chosen as a case study. This paper shows that integrating the developed occupant pattern prediction model within space heating energy management strategies can assist facility managers to achieve space heating energy savings in multi-family residential facilities.     

Forecasting local business activity from aggregate indicators

A regional forecasting technique is developed which combines national and local indicators to provide forecasts of local business activity. A regional business index is first developed to provide a time series from which the historical pattern of local business activity may be evaluated. The relationship between this time series and a similarly constructed national indicator series are first examined with cross-spectral analysis. The information from this analysis is used to guide the formulation of a transfer function time series model. Out-of-sample forecasts are then generated with the transfer model and a univariate ARMIA model. A comparison of these two forecasts to the actual regional index reveals that the transfer model provides a superior forecast.     

昆明市“十四五”时期国土空间规划GDP发展指标预测与分析

作为国土空间规划中的核心指标,地区生产总值(GDP)的预测与分析结果直接影响着地区各项专项规划和未来经济社会的发展,本文依据云南省昆明市历年GDP发展水平、社会状况及昆明市“十四五”时期经济社会发展纲要指标要求,采用非线性预测中的多项式预测与指数函数预测,对昆明市“十四五”时期经济社会发展主要指标中的GDP指标进行预测与分析,在对GDP指标预测的基础上,对预测结果进行修正,实现对昆明市“十四五”时期经济社会发展GDP指标的准确控制及优化,并将最终分析结果和市政府下达的指标进行验证,契合度较高,可见本文采取的方法有较高的可执行性,对正在开展的昆明市“十四五”时期经济社会发展各项主要指标预测与分析有较强的指导和借鉴作用。     

Modeling diffusion of electrical appliances in the residential sector

This paper presents a methodology for modeling residential appliance uptake as a function of root macroeconomic drivers. The analysis concentrates on four major energy end uses in the residential sector: refrigerators, washing machines, televisions and air conditioners. The model employs linear regression analysis to parameterize appliance ownership in terms of household income, urbanization and electrification rates according to a standard binary choice (logistic) function. The underlying household appliance ownership data are gathered from a variety of sources including energy consumption and more general standard of living surveys. These data span a wide range of countries, including many developing countries for which appliance ownership is currently low, but likely to grow significantly over the next decades as a result of economic development. The result is a ‘global’ parameterization of appliance ownership rates as a function of widely available macroeconomic variables for the four appliances studied, which provides a reliable basis for interpolation where data are not available, and forecasting of ownership rates on a global scale. The main value of this method is to form the foundation of bottom-up energy demand forecasts, project energy-related greenhouse gas emissions, and allow for the construction of detailed emissions mitigation scenarios.     

前景理论视角下社会资本参与老旧小区增设电梯路径研究

社会资本参与对老旧住宅小区增设电梯工作推进具有重要意义,引导政府、小区居民和社会资本采用科学合理的策略,是吸引社会资本参与和提升合作效率的重要路径。以前景理论为支撑,以有限理性为前提,构建合作博弈收益感知矩阵,基于该矩阵对参与主体决策行为进行演化博弈分析,为参与主体的决策提供理论参考和改进建议。研究结果表明,小区居民意愿、政府的资金补贴和优惠政策、对低层小区居民赔偿、需求率等因素是影响参与主体决策的重要影响因素;提升小区居民意愿、需求率,科学的政府扶持,以及对低层小区居民进行赔偿有利于促进社会资本参与老旧住宅小区增设电梯的可持续发展。     

A novel cost-optimizing demand response control for a heat pump heated residential building

The present article describes the integration of a data-driven predictive demand response control for residential buildings with heat pump and on-site energy generation. The data driven control approach schedules the heating system of the building. In each day, the next 24 hours heating demand of buildings, including space heating and domestic hot water consumption, are predicted by means of a hybrid wavelet transformation and a dynamic neural network. Linear programming is implemented to define a cost-optimal schedule for the heat pump operation. Moreover, the study discusses the impact of heat demand prediction error on performance of demand response control. In addition, the option of energy trading with the electrical grid is considered in order to evaluate the possibility of increasing the profit for private householders through on-site energy generation. The results highlight that the application of the proposed predictive control could reduce the heating energy cost up to 12% in the cold Finnish climate. Furthermore, on-site energy generation declines the total energy cost and consumption about 43% and 24% respectively. The application of a data-driven control for the demand prediction brings efficiency to demand response control.     

建筑行业工程管理人才预测与分析

通过对影响建筑业人才需求的经济、政策、社会环境等主要因素的分析,研究了各因素对建筑业人才的贡献度,以及内在规律和建筑业人才需求数量规律,构建了建筑业人才二元对数回归预测模型。根据模型预测了今后几年建筑业的人才数量和工程管理人才的需求量,通过问卷调查分析了建筑业对工程管理人员的能力和数量要求,对模型的预测结果进行了印证,并对人才培养提出了有益的建议。     

Performance comparison of occupancy count estimation and prediction with common versus dedicated sensors for building model predictive control

Model predictive control is a promising approach to optimize the operation of building systems and provide demand-response functionalities without compromising indoor comfort. The performance of model predictive control relies, among other things, on the quality of weather forecasts and building occupancy predictions. The present study compares the accuracy and computational demand of two occupancy estimation and prediction approaches suitable for building model predictive control: (1) count prediction based on indoor climate modeling and parameter estimation “using common sensors”, (2) count prediction based on data from 3D stereovision camera. The performance of the two approaches was tested in two rooms of a case study building. The results show that the method with dedicated sensors outperforms common sensors. However, if a building is not equipped with dedicated sensors, the present study shows that the common sensor method can be a satisfactory alternative to be used in model predictive control.     

Near-Term Travel Speed Prediction Utilizing Hilbert–Huang Transform

Abstract:  Accurate short-term prediction of travel speed as a proxy for time is central to many Intelligent Transportation Systems, especially for Advanced Traveler Information Systems and Advanced Traffic Management Systems. In this study, we propose an innovative methodology for such prediction. Because of the inherently direct derivation of travel time from speed data, the study was limited to the use of speed only as a single predictor. The proposed method is a hybrid one that combines the use of the empirical mode decomposition (EMD) and a multilayer feedforward neural network with backpropagation. The EMD is the key part of the Hilbert–Huang transform, which is a newly developed method at NASA for the analysis of nonstationary, nonlinear time series. The rationale for using the EMD is that because of the highly nonlinear and nonstationary nature of link speed series, by decomposing the time series into its basic components, more accurate forecasts would be obtained. We demonstrated the effectiveness of the proposed method by applying it to real-life loop detector data obtained from I-66 in Fairfax, Virginia. The prediction performance of the proposed method was found to be superior to previous forecasting techniques. Rigorous testing of the distribution of prediction errors revealed that the model produced unbiased predictions of speeds. The superiority of the proposed model was also verified during peak periods, midday, and night. In general, the method was accurate, computationally efficient, easy to implement in a field environment, and applicable to forecasting other traffic parameters.     

Clustering a building stock towards representative buildings in the context of air-conditioning electricity demand flexibility

Energy modelling for the prediction of energy use in buildings, especially under novel energy management strategies, is of great importance. In buildings there are several flexible electrical loads which can be shifted in time such as thermostatically controllable loads. The main novelty of this paper is to apply an aggregation method to effectively characterize the electrical energy demand of air-conditioning (AC) systems in residential buildings under flexible operation during demand response and demand shaping programs. The method is based on clustering techniques to aggregate a large and diverse building stock of residential buildings to a smaller, representative ensemble of buildings. The methodology is tested against a detailed simulation model of building stocks in Houston, New York and Los Angeles. Results show good agreement between the energy demand predicted by the aggregated model and by the full model during normal operation (normalized mean absolute error, NMAE, below 10%), even with a small number of clusters (sample size of 1%). During flexible operation, the NMAE rises (around 20%) and a higher number of representative buildings become necessary (sample size at least 10%). Multiple cases for the input data series were considered, namely by varying the time resolution of the input data and the type of input data. These characteristics of the input time series data are shown to play a crucial role in the aggregation performance. The aggregated model showed lower NMAE compared to the original model when clustering is based on a hybrid signal resolved at 60-minute time intervals, which is a combination of the electricity demand profile and AC modulation level.     

Exploring Dual Housing Tenure Status as a Household Response to Demographic,Social and Economic Change

This article proposes that single housing tenure categories do not enable an understanding of the ways in which households use, occupy and own residential properties in the context of broad demographic, economic and social changes. Adapting work on sub-tenure housing choice, housing tenure is overlaid with ownership of residential property to develop four tenure types: Owner, Owner-Owner, Renter and Renter-Owner. Applying this typology in the Australian case provides valuable new insights, with 1.5 million households having dual housing tenure status, including almost one in eight private renters. More broadly, reconceptualising housing tenure to include ownership of other residential property can contribute to theoretical debates about household income and wealth; social status and identity; and social practices and life planning, potentially generating new research questions such as the extent to which Renter-Owners reflect new patterns of living or a response to affordability constraints, and the social identity and political affiliations of those with a dual tenure status.     

A decision tree method for building energy demand modeling

This paper reports the development of a building energy demand predictive model based on the decision tree method. This method is able to classify and predict categorical variables: its competitive advantage over other widely used modeling techniques, such as regression method and ANN method, lies in the ability to generate accurate predictive models with interpretable flowchart-like tree structures that enable users to quickly extract useful information. To demonstrate its applicability, the method is applied to estimate residential building energy performance indexes by modeling building energy use intensity (EUI) levels. The results demonstrate that the use of decision tree method can classify and predict building energy demand levels accurately (93% for training data and 92% for test data), identify and rank significant factors of building EUI automatically. The method can provide the combination of significant factors as well as the threshold values that will lead to high building energy performance. Moreover, the average EUI value of data records in each classified data subsets can be used for reference when performing prediction. One crucial benefit is improving building energy performance and reducing energy consumption. Another advantage of this methodology is that it can be utilized by users without requiring much computation knowledge.